• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.79.3-80
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• 2016

We have investigated the correlation analysis between global temperature anomaly and two main factors: geomagnetic activity (aa index) of Earth external factor and CO2 of Earth internal factor. For this, we used NOAA Global Surface Temperature anomaly (Ta) data from 1868 to 2015. The aa index indicates the geomagnetic activity measured at two anti-podal subauroral stations (Canberra Australia and Hartland England) and the CO2 data come from historical ice core records and NOAA/ESRL data. From the comparison between (Ta) and aa index, we found several interesting things, First, the linear correlation coefficient between two parameters increases until 1985 and then decreases rapidly. Second, the scattered plot between two parameters shows a boundary of the correlation tendency (positive and negative correlation) near 1985. A partial correlation of (Ta) and two main factors (aa index, CO2) also shows that the geomagnetic effect (aa index) is dominant until about 1985 and the CO2 effect becomes much more important after then. These results indicate that the CO2 effect become very an important factor since at least 1985. For a further analysis, we simply assume that Ta = Ta(aa)+Ta(CO2) and made a linear regression between (Ta) and aa index from 1868 to 2015. A linear model is then made from the linear regression between energy consumption (a proxy of CO2 effect) and Ta-Ta(aa) since 1985. Our results will be discussed in view of the prediction of global warming.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.102-106
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• 2005

As long as the effective contributions of satellite images in the continuous monitoring of the wide area and long range of time period, Landsat TM and Landsat ETM+ satellite images are surveyed. After quantization and classification of the deviations between TM and ETM+ images based on approved thresholds such as gains and biases or offsets, a correlation analysis method for the compared calibration is suggested in this paper. Four time points of raster data for 15 years of the highest group of land surface temperature and the lowest group of vegetation of the Kunsan city Chollabuk_do Korea located beneath the Yellow sea coast, are observed and analyzed their correlations for the change detection of urban land cover. This experiment based on proposed algorithm detected strong and proportional correlation relationship between the highest group of land surface temperature and the lowest group of vegetation index which exceeded R=(+)0.9478, so the proposed Correlation Analysis Model between the highest group of land surface temperature and the lowest group of vegetation index will be able to give proof an effective suitability to the land cover change detection and monitoring.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.303-316
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• 2008

For the past 25 years, Korean semiconductor has experienced enormous growth to be the highest production country in the world. Semiconductor industry is very time sensitive and driven by technology and process, and requires 24-hour full operation. The environment includes many different types of equipment, utilities, different gases and toxic chemicals as well as high voltage electricity. We have performed a survey with 3-shift engineers and workers in one line. The content of the questionnaire was about the correlation between fatigue and performance shaping factor (work type and work ability), and as a result we were able to deduce the correlation, p-value and the pattern of scatter plot. The shape of the model was made of 4 blocks for fatigue, 5 blocks for work type and 5 blocks for work ability, i. e. 14 blocks in total. As a conclusion to this findings, there was a correlation between fatigue and work type and work ability specifically in semiconductor industry, and we need some effort to reduce this.

• Nuclear Engineering and Technology
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• v.19 no.2
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• pp.77-84
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• 1987

Statistical evaluation for the heat transfer correlation in the containment subcompartments is carried out from HDR experimental data. Heat transfer data for three HDR blowdown tests, V.42, V.43 and V.44, are analyzed to deduce the correlation. As Uchida already proposed, air-to-steam density ratio is proven to be the most affecting parameter in this study. Here Uchida heat transfer correlation is revised by including temperature difference between the atmosphere and the wall surface, and atmospheric pressure. In addition to these dependencies, atmospheric turbulence and time factor may be included in the model. This implication, however, is not successful, because turbulence and transient phenomena were not adequately quantified in the HDR program. It is concluded that a strong correlation exists between the heat transfer coefficient and temperature differences, specially for forced circulation conditions.

• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.327-354
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• 2019

In this paper, we conducted panel data analysis considering spatial correlation between regions, which were not considered in previous papers in analyzing the causes of fine dust concentration in Korea. Many existing researchers implicitly assume the independence of the effects of incomes and other explanatory variables of adjoining countries(or regions). Using panel data on fine dust concentration, this paper has established that existing EKC can be established even when considering the spatial correlation of the region, and when these effects are not taken into account, it can be underestimated or overestimated on the effects and causes of fine dust concentration.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.57-66
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• 2007

Organizations of Software development are very important issue on enhancement of a software quality as rapid progress of software industry. Especially there are diverse attempts for enhancement of test maturity of the software organization through some kinds of the test maturity model. But the current test maturity models based on CMM(Capability Maturity Model) lack part of actual testing measurement and only measure level of test maturity. To solve these problems, we suggest 'double V-model' to execute both software development process and test process simultaneously, and also 'test attributes to Maturity Levels Correlation Matrix' for evaluating level of test maturity included with definitions of test attribute and level. That is, we enhance TMM(Test Maturity Model) adopted with 'Improvement Suggestion' of TPI(Test Process Improvement) which is easy the evaluation of test maturity of organization and gives the direction of improvement to level up the test maturity for the measured organization. As a result, we will contribute to level up the test maturity of the organization.

• Journal of Environmental Health Sciences
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• v.32 no.5 s.92
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• pp.506-514
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• 2006

The objectives of this study were to evaluate personal exposure estimated using a time activity pattern and microenvironmental model. The study was carried out for 44 children attending a primary school nearby the lines (school A) and 125 children attending a school away from 154 kV power lines (school B). For children attending school A, the estimated personal level was a little weak correlated with the measured level($Pearson\;r\;=0.34{\sim}0.35$). For children attending school B, the correlation was very low ($Pearson\;r\;=\;0.09{\sim}0.16$) using the TW A Model II, otherwise, TWA Model II-I which considered the average residential MF level according to the distance from the power line and home explained $39{\sim}53%$ of the correlation in MF personal exposures. The estimated personal exposure level was very well represented by the measured exposure level using TWA Model II-2 which consisted on spot and 24 h stationary measurements at subject's home ($Pearson\;r\;=\;0.65{\sim}0.85$). In conclusion, personal magnetic field expsoure estimated using a TWA Model II-2 should be provided for a reasonable estimate of measured exposure in schoolchildren living near the power line.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.327-340
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• 2015

General Circulation Models (GCMs) are the basic tool used for modelling climate. However, the spatio-temporal discrepancy between GCM and observed value, therefore, the models deliver output that are generally required calibration for applied studies. Which is generally done by Multi-Model Ensemble (MME) approach. Stochastic downscaling methods have been used extensively to generate long-term weather sequences from finite observed records. A primary objective of this study is to develop a forecasting scheme which is able to make use of a MME of different GCMs. This study employed a Nonstationary Hidden Markov Chain Model (NHMM) as a main tool for downscaling seasonal ensemble forecasts over 3 month period, providing daily forecasts. Our results showed that the proposed downscaling scheme can provide the skillful forecasts as inputs for hydrologic modeling, which in turn may improve water resources management. An application to the Nakdong watershed in South Korea illustrates how the proposed approach can lead to potentially reliable information for water resources management.

• Magazine of the Korean Society of Agricultural Engineers
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• v.24 no.1
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• pp.63-72
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• 1982

To maintain an optimum condition for the plant growth on upland soil, the irrigation planning after the natural rainfall should be given enormous considerations on the rainfall effectiveness. This study has been intended to develop the computer model for estimating the effec- tiveness of the rainfall. The computer model should also estimated the infiltration due to the rainfall and the soil moisture deficiency at the root zone of the plant. For this purpose, the experiments of infiltration using rainfall simulator and the observations of the change of soil moisture content before and after rainfall were carried out. Needed input data for the developed model include final infiltration capacity and field capacity of the soil, porosity of the top soil, root depth of the plant, rainfall intensity and duration, and the Horton's decay coefficient. Among the needed input data for the developed model, final infiltration capacity and Horton's decay coefficient were determined by the experiments of infiltration. And from the result of the experiments, it is found that there is a great correlation between initial infiltration capacity and initial moisture content. And it is also found that the infiltration due to rainfall can be estimated with the Horton's equation. The developed model was tested by the experimental data with two rainfall intensities. Tests were conducted on the different root depths at each rainfall. Observed and estimated effective rainfalls were found to have great correlation. The result of the experiments showed that the effectiveness of the rainfall were 100%, so the comparisons were conducted by the comsumption rates of infiltration at each depth. The developed model can be also used for estimating the deficiency of rainfall, if the rainfall is not sufficient to the needed soil moisture. But, test was not carried out.

• Journal of Environmental Science International
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• v.24 no.6
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• pp.743-753
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• 2015

In this study, it is an object to develop a regression model for the estimation of TOC (total organic carbon) concentration using investigated data for three years from 2010 to 2012 in the Gam Stream unit watershed, and applied in 2009 to verify the applicability of the regression model. TOC and $COD_{Mn}$ (chemical oxygen demand) were appeared to be derived the highest correlation. TOC was significantly correlated with 5 variables including BOD (biological oxygen demand), discharge, SS (suspended solids), Chl-a (chlorophyll a) and TP (total phosphorus) of p<0.01. As a result of PCA (principal component analysis) and FA (factor analysis), COD, TOC, SS, discharge, BOD and TP have been classified as a first factor. TOCe concentration was estimated using the model developed as an independent variable $BOD_5$ and $COD_{Mn}$. R squared value between TOC and measurement TOC is 0.745 and 0.822, respectively. The independent variable were added step by step while removing lower importance variable. Based on the developed optimal model, R squared value between measurement value and estimation value for TOC was 0.852. It was found that multiple independent variables might be a better the estimation of TOC concentration using the regression model equation(in a given sites).